Engineering Application of Artificial Intelligence and Machine Learning
Disciplines: Data Science and Engineering Analytics
It is very important to learn what are the main characteristics and requirements in applying Artificial Intelligence and Machine Learning to solve Engineering related problems. To learn and apply Science and Engineering, Homo Sapiens must take a serious amount of time and efforts at universities and industries. Unfortunately, large number of individuals that are interested in Data Science do not necessarily think that the same think is required for solving scientific and engineering related problems using Artificial Intelligence and Machine Learning.
Furthermore, issues such as the differences between traditional Statistics and Artificial Intelligence and Machine Learning, Explainable Artificial Intelligence, and Ethics of Artificial Intelligence are also very importance parts of the Engineering Application of Artificial Intelligence that will be covered in this Course.
Topics covered in this short course:
- Artificial Intelligence & Machine Learning
- Modeling Physical Phenomena using Artificial Intelligence
- Differences between Engineering and Non-Engineering Application of Artificial Intelligence
- Differences between Traditional Statistics and Artificial Intelligence
- eXplainable Artificial Intelligence
- AI-Ethics (Why we should not be using Hybrid Models)
Intermediate to Advanced
This course will play a crucial role for the enthusiasts of engineering application of Artificial Intelligence and Machine Learning technology in Reservoir Simulation and Modeling. It covers the scientific and realities foundation of Artificial Intelligence and Machine Learning and its true application in Reservoir Engineering. If you are interested to be knowledgeable with the most up-to-date and accurate AI and Machine Learning technology? This class will get you there!
This course is designed for geo-scientists, reservoir engineers, and managers. Specifically, those involved with geology, reservoir, completion, and production in operating and service companies. In general, those involved in planning, completion, and operation in assets are the main target audience.
Students should bring a calculator and their laptop.
0.8 CEUs are awarded for this 1-day course.
All cancellations must be received no later than 14 days prior to the course start date. Cancellations made after the 14-day window will not be refunded. Refunds will not be given due to no show situations.
Training sessions attached to SPE conferences and workshops follow the cancellation policies stated on the event information page. Please check that page for specific cancellation information.
SPE reserves the right to cancel or re-schedule courses at will. Notification of changes will be made as quickly as possible; please keep this in mind when arranging travel, as SPE is not responsible for any fees charged for cancelling or changing travel arrangements.
We reserve the right to substitute course instructors as necessary.
Dr. Shahab D. Mohaghegh, a pioneer in the application of Artificial Intelligence and Machine Learning in the Exploration and Production industry, is Professor of Petroleum and Natural Gas Engineering at West Virginia University and the president and CEO of Intelligent Solutions, Inc. (ISI). He is the director of WVU-LEADS (Laboratory for Engineering Application of Data Science).
Including more than 30 years of research and development in the petroleum engineering application of Artificial Intelligence and Machine Learning, he has authored three books (Shale Analytics – Data Driven Reservoir Modeling – Application of Data-Driven Analytics for the Geological Storage of CO2), more than 200 technical papers and carried out more than 60 projects for independents, NOCs and IOCs. He is a SPE Distinguished Lecturer (2007 and 2020) and has been featured four times as the Distinguished Author in SPE’s Journal of Petroleum Technology (JPT 2000 and 2005). He is the founder of SPE’s Technical Section dedicated to AI and machine learning (Petroleum Data-Driven Analytics, 2011).
He has been honored by the U.S. Secretary of Energy for his AI-based technical contribution in the aftermath of the Deepwater Horizon (Macondo) incident in the Gulf of Mexico (2011) and was a member of U.S. Secretary of Energy’s Technical Advisory Committee on Unconventional Resources in two administrations (2008-2014). He represented the United States in the International Standard Organization (ISO) on Carbon Capture and Storage technical committee (2014-2016).